Cell Lineage Tracing in Lens-Free Microscopy Videos

Rempfler, Markus; Kumar, Sanjeev; Stierle, Valentin; Paulitschke, Philipp; Andres, Bjoern; Menze, Bjoern

doi:10.1007/978-3-319-66185-8_1

Cited by 12 publications

(11 citation statements)

References 14 publications

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“…Our approach could be particularly beneficial in the context of cell tracking. There, it is often desirable to have multiple diverse segmentation hypotheses [13,8], which could be achieved by suppressing fewer candidate polygons. Furthermore, StarDist can plausibly complete shapes for partially visible cells at the image boundary, which could make it easier to track cells that enter and leave the field of view over time.…”

Section: Discussionmentioning

confidence: 99%

Cell Detection with Star-Convex Polygons

Schmidt

Weigert

Broaddus

et al. 2018

Lecture Notes in Computer Science

1,046

952

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Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications. Recent successful learning-based approaches include per-pixel cell segmentation with subsequent pixel grouping, or localization of bounding boxes with subsequent shape refinement. In situations of crowded cells, these can be prone to segmentation errors, such as falsely merging bordering cells or suppressing valid cell instances due to the poor approximation with bounding boxes. To overcome these issues, we propose to localize cell nuclei via star-convex polygons, which are a much better shape representation as compared to bounding boxes and thus do not need shape refinement. To that end, we train a convolutional neural network that predicts for every pixel a polygon for the cell instance at that position. We demonstrate the merits of our approach on two synthetic datasets and one challenging dataset of diverse fluorescence microscopy images.

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Section: Discussionmentioning

confidence: 99%

Cell Detection with Star-Convex Polygons

Schmidt

Weigert

Broaddus

et al. 2018

Lecture Notes in Computer Science

1,046

952

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show abstract

“…More recently, an FCN network with two sibling branches is proposed for simultaneous nucleus detection and classification [54] and the joint learning allows both tasks to benefit from each other. Another FCN-based cell detection method can be found in [55], where it introduces deconvolutional layers to the ResNet [56] such that the output probability map has an identical dimension as the input image.…”

Section: Introductionmentioning

confidence: 99%

Towards pixel-to-pixel deep nucleus detection in microscopy images

et al. 2019

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Background Nucleus or cell detection is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed. Results We analyze the applicability of deep models specifically for nucleus detection across a wide variety of microscopy image data. More specifically, we present a fully convolutional network-based regression model and extensively evaluate it on large-scale digital pathology and microscopy image datasets, which consist of 23 organs (or cancer diseases) and come from multiple institutions. We demonstrate that for a specific target dataset, training with images from the same types of organs might be usually necessary for nucleus detection. Although the images can be visually similar due to the same staining technique and imaging protocol, deep models learned with images from different organs might not deliver desirable results and would require model fine-tuning to be on a par with those trained with target data. We also observe that training with a mixture of target and other/non-target data does not always mean a higher accuracy of nucleus detection, and it might require proper data manipulation during model training to achieve good performance. Conclusions We conduct a systematic case study on deep models for nucleus detection in a wide variety of microscopy images, aiming to address several important but previously understudied questions. We present and extensively evaluate an end-to-end, pixel-to-pixel fully convolutional regression network and report a few significant findings, some of which might have not been reported in previous studies. The model performance analysis and observations would be helpful to nucleus detection in microscopy images.

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“…Even though the U-Net is a popular architecture in the recent audio processing literature, to the best of our knowledge, no work in the domain of audio processing compares the U-Net against the well-established baseline of the ResNet. A small number of comparative studies exist in the fields of image processing and medical imaging, in which either the number of parameters of the compared models is not stated [48], or in which the ResNet has significantly fewer parameters than the U-Net [49], [50], [51]. In all these works, the ResNet outperforms the U-Net by a small margin.…”

Section: B Contributionsmentioning

confidence: 99%

On Filter Generalization for Music Bandwidth Extension Using Deep Neural Networks

Sulun

Davies

2021

IEEE J. Sel. Top. Signal Process.

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In this paper, we address a sub-topic of the broad domain of audio enhancement, namely musical audio bandwidth extension. We formulate the bandwidth extension problem using deep neural networks, where a band-limited signal is provided as input to the network, with the goal of reconstructing a fullbandwidth output. Our main contribution centers on the impact of the choice of low pass filter when training and subsequently testing the network. For two different state of the art deep architectures, ResNet and U-Net, we demonstrate that when the training and testing filters are matched, improvements in signalto-noise ratio (SNR) of up to 7 dB can be obtained. However, when these filters differ, the improvement falls considerably and under some training conditions results in a lower SNR than the bandlimited input. To circumvent this apparent overfitting to filter shape, we propose a data augmentation strategy which utilizes multiple low pass filters during training and leads to improved generalization to unseen filtering conditions at test time.

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Cell Lineage Tracing in Lens-Free Microscopy Videos

Cited by 12 publications

References 14 publications

Cell Detection with Star-Convex Polygons

Cell Detection with Star-Convex Polygons

Towards pixel-to-pixel deep nucleus detection in microscopy images

On Filter Generalization for Music Bandwidth Extension Using Deep Neural Networks

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